Papers to Appear in Subsequent Issues

Testing in High-Dimensional Spiked Models Iain M Johnstone and Alexei Onatski
Distributed Estimation of Principal Eigenspaces Jianqing Fan, Dong Wang, Kaizheng Wang, and Ziwei Zhu
Sorted Concave Penalized Regression Long Feng and Cun-Hui Zhang
Active Ranking from Pairwise Comparisons and When Parametric Assumptions Don’t Help Reinhard Heckel, Nihar B. Shah, Kannan Ramchandran, and Martin J. Wainwright
Randomized incomplete U-statistics in high dimensions Xiaohui Chen and Kengo Kato
Adaptive estimation of the rank of the coefficient matrix in high dimensional multivariate response regression models Xin Bing and Marten Wegkamp
Statistical inference for autoregressive models under heteroscedasticity of unknown form Ke Zhu
On Partial-Sum Processes of ARMAX Residuals Steffen Grønneberg and Benjamin Holcblat
Quantile Regression Under Memory Constraint Xi Chen, Weidong Liu, and Yichen Zhang
Sampling and Estimation for (Sparse) Exchangeable Graphs Victor Veitch and Daniel Murphy Roy
Hypothesis Testing on Linear Structures of High Dimensional Covariance Matrix Shurong Zheng, Zhao Chen, Hengjian Cui, and Runze Li
On optimal designs for non-regular models Yi Lin, Ryan Martin, and Min Yang
A Smeary Central Limit Theorem for Manifolds with Application to High Dimensional Spheres Benjamin Eltzner and Stephan F. Huckemann
On testing for high-dimensional white noise Zeng Li, Jianfeng Yao, Clifford Lam,  and Qiwei Yao
Minimax Posterior Convergence Rates and Model Selection Consistency in High-dimensional DAG Models based on Sparse Cholesky Factors Kyoungjae Lee, Jaeyong Lee, and Lizhen Lin
Bootstrapping and Sample Splitting for High-Dimensional, Assumption-Free Inference Alessandro Rinaldo, Max G’Sell, Jing Lei, and Larry Wasserman
Joint convergence of sample autocovariance matrices when p/n → 0 with application Monika Bhattacharjee and Arup Bose
Tracy-Widom limit for Kendall’s tau Zhigang Bao
Intrinsic Riemannian Functional Data Analysis Zhenhua Lin and Fang Yao
Two-Step Semiparametric Empirical Likelihood Inference Francesco Bravo, Juan Carlos Escanciano, and Ingrid Van Keilegom
The Phase Transition for the Existence of the Maximum Likelihood Estimate in High-Dimensional Logistic Regression Emmanuel Jean Candes and Pragya Sur
Rerandomization in 2K Factorial Experiments Peng Ding, Xinran Li, and Donald Bruce Rubin
Sparse Sir: Optimal Rates and Adaptive Estimation Kai Tan, Lei Shi, and Zhou Yu
On Estimation of Isotonic Piecewise Constant Signals Chao Gao, Fang Han, and Cun-Hui Zhang
Robust Sparse Covariance Estimation by Thresholding Tyler’s M-Estimator John Goes, Gilad Lerman, and Boaz Nadler
Model-assisted variable clustering: minimax-optimal recovery and algorithms Florentina Bunea, Christophe Giraud, Martin Royer, Nicolas Verzelen, and Xi Luo
The New G-Formula for the Sequential Causal Effect and the Blip Effect of Treatment in Sequential Causal Inference Xiaoqin Wang and Li Yin
Envelope-Based Sparse Partial Least Squares Guangyu Zhu and Zhihua Su
Optimal Rates for Community Estimation in the Weighted Stochastic Block Model Min Xu, Varun Jog, and Po-Ling Loh
Limiting Laws for Divergent Spiked Eigenvalues and Largest Non-spiked Eigenvalue of Sample Covariance Matrices Tony Cai, Xiao Han, and Guangming Pan
Spatial Adaptation in Trend Filtering Adityanand Guntuboyina, Donovan Lieu, Sabyasachi Chatterjee, and Bodhisattva Sen
Spectral and matrix factorization methods for consistent community detection in multi-layer networks Subhadeep Paul and Yuguo Chen
Statistical Inference for Model Parameters in Stochastic Gradient Descent Xi Chen, Jason D. Lee, Xin T. Tong, and Yichen Zhang
Non-classical Berry-Esseen inequalities and accuracy of the bootstrap Mayya Zhilova
Bootstrap Confidence Regions based on M-Estimators under Nonstandard Conditions Stephen M.S. Lee and Puyudi Yang
Sparse high dimensional regression: Exact scalable algorithms and phase transitions Dimitris Bertsimas and Bart van Parys
Testing for Principal Component Directions under Weak Identifiability Davy Paindaveine, Julien Rémy, and Thomas Verdebout
Multidimensional multiscale scanning in Exponential Families: Limit theory and statistical consequences Claudia König, Axel Munk, and Frank Werner
Designs for estimating the treatment effect in networks with interference Ravi Jagadeesan, Natesh Pillai, and Alexander Volfovsky
Learning a Tree-Structured Ising Model in Order to Make Predictions Guy Bresler and Mina Karzand
The multi-armed bandit problem: an efficient non-parametric solution Hock Peng Chan
Concentration and Consistency Results for Canonical and Curved Exponential-Family Models of Random Graphs Michael Schweinberger and Jonathan Stewart
Change point analysis in non-stationary processes – a mass excess approach Holger Dette and Weichi Wu
The Numerical Bootstrap Han Hong and Jessie Li
On the optimality of sliced inverse regression in high dimensions Qian Lin, Jun S Liu, Dongming Huang, and Xinran Li
Consistent Selection of the Number of Change-Point Via Sample-Splitting Changliang Zou, Guanghui Wang, and Runze Li
Uniformly valid confidence intervals post-model-selection Francois Bachoc, David Preinerstorfer, and Lukas Steinberger
Efficient Estimation of Linear Functionals of Principal Components Vladimir Koltchinskii, Matthias Loeffler, and Richard Nickl
On the nonparametric maximum likelihood estimator for Gaussian location mixture densities with application to Gaussian denoising Sujayam Saha and Adityanand Guntuboyina
Prediction error after model search Xiaoying Tian
Optimal Prediction in the Linearly Transformed Spiked Model Edgar Dobriban, William Leeb, and Amit Singer
Averages of Unlabeled Networks: Geometric Characterization and Asymptotic Behavior Eric Kolaczyk, Lizhen Lin, Steven Rosenberg, Jackson Walters, and Jie Xu
Markov equivalence of marginalized local independence graphs Søren Wengel Mogensen and Niels Richard Hansen
Joint estimation of parameters in Ising model Promit Ghosal and Sumit Mukherjee
Asymptotic genealogies of interacting particle systems with an application to sequential Monte Carlo Jere Koskela, Paul Jenkins, Adam Johansen, and Dario Spano
Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data Zhiqiang Tan
Hurst Function Estimation Tailen Hsing and Jinqi Shen
Detection limits in the spiked Wigner model Ahmed El Alaoui, Florent Krzakala, and Michael Jordan
α-Variational Inference with Statistical Guarantees Yun Yang, Debdeep Pati, and Anirban Bhattacharya
Robust machine learning by median-of-means: theory and practice Guillaume Lecué and Matthieu Lerasle
Almost Sure Uniqueness of a Global Minimum Without Convexity Gregory Cox
Consistent Maximum Likelihood Estimation Using Subsets with Applications to Multivariate Mixed Models Karl Oskar Ekvall and Galin L. Jones
Asymptotic Optimality in Stochastic Optimization John Duchi and Feng Ruan
Convergence of eigenvector empirical spectral distribution of sample covariance matrices Jun Yin, Haokai Xi, Fan Yang
Additive Models with Trend Filtering Veeranjaneyulu Sadhanala, and Ryan Joseph Tibshirani
D-optimal Designs for Multinomial Logistic Models Xianwei Bu, Dibyen Majumdar, and Jie Yang
A unified study of nonparametric inference for monotone functions Ted Westling and Marco Carone
Inference for Archimax copulas Simon Chatelain, Anne-Laure Fougères, and Johanna G. Neslehova
Admissible Bayes equivariant estimation of location vectors for spherically symmetric distributions with unknown scale Yuzo Maruyama and William E. Strawderman
Worst-case vs Average-case Design for Estimation from Partial Pairwise Comparisons Ashwin Pananjady, Cheng Mao, Vidya Muthukumar, Martin J. Wainwright, and Thomas A. Courtade
Non-asymptotic upper bounds for the reconstruction error of PCA Martin Wahl and Markus Reiß
Lasso Guarantees for β-Mixing Heavy Tailed Time Series Kam Chung Wong, Zifan Li, and Ambuj Tewari
High-frequency analysis of parabolic stochastic PDEs Carsten Chong
Functional data analysis in the Banach space of continuous functions Holger Dette, Kevin Kokot, and Alexander Aue
Mean Estimation with Sub-Gaussian Rates in Polynomial Time Samuel Hopkins
Bootstrapping Max Statistics in High Dimensions: Near-Parametric Rates Under Weak Variance Decay and Application to Functional and Multinomial Data Miles Lopes, Zhenhua Lin, and Hans-Georg Mueller
Empirical Bayes oracle uncertainty quantification for regression Eduard Belitser and Subhashis Ghosal
GRID: A variable selection and structure discovery method for high dimensional nonparametric regression Francesco Giordano, Soumendra Nath Lahiri, and Maria Lucia Parrella
Post Hoc Confidence Bounds on False Positives Using Reference Families Gilles Blanchard, Pierre Neuvial, and Etienne Roquain
Distribution and Correlation Free Two-sample Test of High-dimensional Means Kaijie Xue and Fang Yao
Just Interpolate: Kernel “Ridgeless” Regression Can Generalize Tengyuan Liang and Alexander Rakhlin
Bridging the Gap between Constant Step Size Stochastic Gradient Descent and  Markov Chains Alain Durmus, Aymeric Dieuleveut, and Francis Bach
Nonparametric statistical inference for drift vector fields of multi-dimensional diffusions Richard Nickl and Kolyan Ray
Robust inference with knockoffs Rina Foygel Barber, Emmanuel J Candes, and Richard J Samworth
Nonparametric Bayesian Analysis of the Compound Poisson Prior For Support Boundary Recovery Johannes Schmidt-Hieber and Markus Reiss
Entrywise Eigenvector Analysis of Random Matrices with Low Expected Rank Emmanuel Abbe, Jianqing Fan, Kaizheng Wang, and Yiqiao Zhong
Concentration of tempered posteriors and of their variational approximations Pierre Alquier and James Ridgway
Robust and rate-optimal Gibbs posterior inference on the boundary of a noisy image Nicholas Aaron Syring and Ryan Martin
The Hardness of Conditional Independence Testing and the Generalised Covariance Measure Rajen Dinesh Shah and Jonas Peters
Some Theoretical Properties of GANs Gerard Biau, Cadre Benoit, Sangnier Maxime, and Ugo Tanielian
On post dimension reduction statistical inference Kyongwon Kim, Bing Li, Zhou Yu, and Lexin Li
Statistical and Computational Limits for Sparse Matrix Detection T. Tony Cai and Yihong Wu
Segmentation and estimation of change-point models David O. Siegmund, Xiao Fang, and Jian Li
Robust Covariance Estimation Under L4 − L2 Norm Equivalence Shahar Mendelson and Nikita Zhivotovskiy
Robust inference via multipler bootstrap Xi Chen and Wen-Xin Zhou
On the Optimal Reconstruction of Partially Observed Functional Data Alois Kneip and Dominik Liebl
Large Sample Properties of Partitioning-Based Series Estimators Matias D. Cattaneo, Max H. Farrell, and Yingjie Feng
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score Qingyuan Zhao, Jingshu Wang, Gibran Hemani, Jack Bowden, and Dylan S Small
Local Uncertainty Sampling for Large-Scale Multi-Class Logistic Regression Lei Han, Kean Ming Tan, Ting Yang, and Tong Zhang
Local nearest neighbour classification with applications to semi-supervised learning Timothy Ivor Cannings, Thomas Benjamin Berrett, and Richard John Samworth
An adaptable generalization of Hotelling’s T2 test in high dimension Haoran Li, Alexander Aue, Debashis Paul, Jie Peng, and Pei Wang
Penalized Generalized Empirical Likelihood with a Diverging Number of General Estimating Equations for Censored Data Niansheng Tang, Xiaodong Yan, and Xingqiu Zhao
On the validity of the formal Edgeworth expansion for posterior densities John E. Kolassa and Todd A. Kuffner
Model selection for high-dimensional linear regression with dependent observations Ching-Kang Ing
Optimal estimation of Gaussian mixtures via denoised method of moments Yihong Wu and Pengkun Yang
Sharp Instruments for Classifying Compliers and Generalizing Causal Effects Edward H Kennedy, Sivaraman Balakrishnan, and Max G’Sell
Nonparametric regression using deep neural networks with ReLU activation function Johannes Schmidt-Hieber
Empirical risk minimization and complexity of dynamical models Kevin McGoff and Andrew B. Nobel
Adaptive Estimation in  Structured  Factor Models with Applications to Overlapping Clustering Florentina Bunea, Mike Bing, Yang Ning, and Marten Wegkamp
Partial Identifiability of Restricted Latent Class Models Yuqi Gu and Gongjun Xu
Posterior Concentration for Bayesian Regression Trees and Their Ensembles Veronika Rockova and Stephanie van der Pas
Double-Slicing Assisted Sufficient Dimension Reduction for High Dimensional Censored Data Shanshan Ding, Wei Qian, and Lan Wang
Asymptotic frequentist coverage properties of Bayesian credible sets for sieve priors Judith Rousseau and Botond Szabo
Asymptotic joint distribution of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model Zeng Li, Fang Han, Jianfeng Yao
Convergence Rates of Variational Posterior Distributions Fengshuo Zhang and Chao Gao
Two-sample Hypothesis Testing for Inhomogeneous Random Graphs Debarghya Ghoshdastidar, Maurilio Gutzeit, Alexandra Carpentier, and Ulrike von Luxburg
Beyond HC: More sensitive tests for rare/weak alternatives Thomas Porter and Michael Stewart
Minimax optimal rates for Mondrian trees and forests Jaouad Mourtada, Stéphane Gaïffas, and Erwan Scornet
Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering Bryon Aragam, Chen Dan, Eric Xing, and Pradeep Ravikumar
A test for separability in covariance operators of random surfaces Pramita Bagchi and Holger Dette
A General Approach for Cure Models in Survival Analysis Valentin Patilea and Ingrid Van Keilegom
Adaptive distributed methods under communication constraints Botond Szabo and Harry van Zanten
Bayesian Analysis of the Covariance Matrix of a Multivariate Normal Distribution with a New Class of Priors James O Berger, Dongchu Sun, and Chengyuan Song
Extending the Validity of Frequency Domain Bootstrap Methods to General Stationary Processes Efstathios Paparoditis, Marco Meyer, and Jens-Peter Kreiss
Minimax  Estimation of Large Precision Matrices with Bandable Cholesky Factor Yu Liu and Zhao Ren
Estimation And Inference for Precision Matrices of Non-stationary Time Series Xiucai Ding and Zhou Zhou
Testing for stationarity of functional time series in the frequency domain Alexander Aue and Anne van Delft
Isotropic covariance functions on graphs and their edges Ethan Anderes, Jesper Møller, and Jakob Rasmussen
On spike and slab empirical Bayes multiple testing Ismael Castillo and Etienne Roquain
Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection Anderson Y. Zhang and Harrison H. Zhou
Minimax Optimal Sequential Hypothesis Tests for Markov Processes Michael Fauss, Abdelhak Zoubir, and Harold Vincent Poor
Test of Significance for High-Dimensional Longitudinal Data Ethan X. Fang, Yang Ning,and Runze Li
Geometrizing  rates of convergence under local differential privacy constraints Angelika Rohde and Lukas Steinberger
Additive Regression with Hilbertian Responses Jeong Min Jeon and Byeong U Park
Nonparametric Bayesian Estimation of Multivariate Hawkes Processes Sophie Donnet, Vincent Rivoirard, and Judith Rousseau
Self-normalization for high dimensional time series Runmin Wang and Xiaofeng Shao
Variational Analysis of Constrained M-Estimators Johannes O Royset and Roger J-B Wets
Which Bridge Estimator is the Best for Variable Selection? Shuaiwen Wang, Haolei Weng, and Arian Maleki
Permutation methods for factor analysis and PCA Edgar Dobriban
Concordance and Value Information Criteria for Optimal Treatment Decision Chengchun Shi, Rui Song, and Wenbin Lu
A General Framework for Bayes Structured Linear Models Chao Gao, Aad van der Vaart, and Harrison Zhou
Discussion of “Nonparametric Regression using Deep Neural Networks with ReLU Activation Function” Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, and Andrea Montanari
Discussion of “Nonparametric Regression using Deep Neural Networks with ReLU Activation Function” Gitta Kutyniok